# Private Federated Statistics in an Interactive Setting

arXiv: https://arxiv.org/abs/2211.10082 · HTML: https://mlsanity.com/paper/2211.10082/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2022.

## Built on by (most cited first, 5 of 5)

- [One-Shot Federated Conformal Prediction](https://mlsanity.com/paper/2302.06322/) (2023)
- [SoK: A Review of Differentially Private Linear Models For High-Dimensional Data](https://mlsanity.com/paper/2404.01141/) (2024)
- [Differentially Private Heavy Hitter Detection using Federated Analytics](https://mlsanity.com/paper/2307.11749/) (2023)
- [Population Expansion for Training Language Models with Private Federated Learning](https://mlsanity.com/paper/2307.07477/) (2023)
- [Randomized algorithms for precise measurement of differentially-private, personalized recommendations](https://mlsanity.com/paper/2308.03735/) (2023)

## Abstract (arXiv)

Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interactivity. We argue that an aggregation protocol can enable an interactive private federated statistics system where user's devices maintain control of the privacy assurance. We describe the architecture of such a system, and analyze its security properties.